An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device
Considering that the vibration signals of gears and bearings in the automatic transmission device are complex and the fault features are difficult to extract. This paper proposes a method for extracting fault features of transmission device using adaptive variational modal decomposition (AVMD), and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9438048/ |
| _version_ | 1819002840380604416 |
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| author | Feng Ding Yuan Xia Jianhui Tian Xinrui Zhang Guangchu Hu |
| author_facet | Feng Ding Yuan Xia Jianhui Tian Xinrui Zhang Guangchu Hu |
| author_sort | Feng Ding |
| collection | DOAJ |
| description | Considering that the vibration signals of gears and bearings in the automatic transmission device are complex and the fault features are difficult to extract. This paper proposes a method for extracting fault features of transmission device using adaptive variational modal decomposition (AVMD), and uses deep belief network (DBN) for pattern recognition. The vibration signal is decomposed by AVMD using energy ratio method. The intrinsic mode function (IMF) with abundant fault information is obtained. By calculating the energy entropy of each IMF component and form a high-dimensional feature vector as the input of DBN to establish an early fault identification model. The early fault data of the PHM2009 transmission device experimental platform was selected for identification and analysis. The identification results show that AVMD can extract the weak features of transmission device fault signals more accurately than empirical mode decomposition (EMD). Moreover, DBN has a higher fault identification accuracy rate than support vector machine (SVM), probabilistic neural network (PNN), back propagation neural network (BP) and Kohonen self-organizing competition neural network. |
| first_indexed | 2024-12-20T23:11:30Z |
| format | Article |
| id | doaj.art-a349bf0c6f33449b8f77d2d17fdf7217 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T23:11:30Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a349bf0c6f33449b8f77d2d17fdf72172022-12-21T19:23:43ZengIEEEIEEE Access2169-35362021-01-01915008815009710.1109/ACCESS.2021.30792379438048An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission DeviceFeng Ding0https://orcid.org/0000-0002-5921-3102Yuan Xia1https://orcid.org/0000-0002-4166-7069Jianhui Tian2Xinrui Zhang3https://orcid.org/0000-0002-1303-1899Guangchu Hu4School of Mechanical and Electrical Engineering, Xi’an Technological University, Xi’an, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Technological University, Xi’an, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Technological University, Xi’an, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Technological University, Xi’an, ChinaXi’an Advanced Dynamics Software Development Company Ltd., Xi’an, ChinaConsidering that the vibration signals of gears and bearings in the automatic transmission device are complex and the fault features are difficult to extract. This paper proposes a method for extracting fault features of transmission device using adaptive variational modal decomposition (AVMD), and uses deep belief network (DBN) for pattern recognition. The vibration signal is decomposed by AVMD using energy ratio method. The intrinsic mode function (IMF) with abundant fault information is obtained. By calculating the energy entropy of each IMF component and form a high-dimensional feature vector as the input of DBN to establish an early fault identification model. The early fault data of the PHM2009 transmission device experimental platform was selected for identification and analysis. The identification results show that AVMD can extract the weak features of transmission device fault signals more accurately than empirical mode decomposition (EMD). Moreover, DBN has a higher fault identification accuracy rate than support vector machine (SVM), probabilistic neural network (PNN), back propagation neural network (BP) and Kohonen self-organizing competition neural network.https://ieeexplore.ieee.org/document/9438048/AVMDDBNfeature extractionfault identification |
| spellingShingle | Feng Ding Yuan Xia Jianhui Tian Xinrui Zhang Guangchu Hu An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device IEEE Access AVMD DBN feature extraction fault identification |
| title | An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device |
| title_full | An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device |
| title_fullStr | An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device |
| title_full_unstemmed | An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device |
| title_short | An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device |
| title_sort | avmd method based on energy ratio and deep belief network for fault identification of automation transmission device |
| topic | AVMD DBN feature extraction fault identification |
| url | https://ieeexplore.ieee.org/document/9438048/ |
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